Common Logic in Support of Metadata and Ontologies Open Forum 2005 on Metadata Registries 14:45 Track 1 14 April 2005 Harry S. Delugach Intelligent Systems Laboratory University of Alabama in Huntsville, USA

Intelligent Systems Laboratory OutlineOutline Common Logic –Background –First order semantics –ISO standard features Representative syntax: Conceptual Graphs Interchange –Issues Example

Intelligent Systems Laboratory OverviewOverview First order logic language for knowledge interchange Provides a core semantic framework for logic Provides the basis for a set of syntactic forms (dialects) all sharing a common semantics

Intelligent Systems Laboratory Common Logic Participants SCL ad-hoc working group (formed Dec 2002): Pat Hayes IHMC, USA Chris MenzelTexas A&M U., USA John Sowa VivoMind, USA Tanel Tammet U. Goteborg, Sweden Bill Andersen OntologyWorks, USA Murray Altheim Open University, UK Harry Delugach U. Alabama Huntsville, USA Michael Gruninger NIST, USA (and Canada)

Intelligent Systems Laboratory Origins of Common Logic Conceptual Graphs, 1984 –Linear (textual form) –Display (graphic form) –Natural language processing, knowledge based systems Knowledge Interchange Format (KIF) c –Part of the Ontolingua project at Stanford to develop ontologies KIF-CGIF collaboration, Common Logic (CL) Simplified Common Logic (SCL) 2002-present ISO Project (Common Logic) starting June 2003

Intelligent Systems Laboratory First Order Semantics A statement/sentence/assertion is considered completely true or completely false –My name is Harry Delugach –NOT = 5 Compare: Logic is easy to learn. Entities - things, states, attributes –Harry, idleness, color, etc. Relations - between entities, attributes –Marriage, eye-color, etc. Quantification - single instance or a set ( ,  ) –Definition, uniqueness, etc. Negation - explicit falsehood –Harry is not President of the United States Iteration - over elements a set –Age of each member of a population

Intelligent Systems Laboratory Comparing Formalisms Formalisms can be arranged by their expressivity (“power”) –The set of things that can possibly be expressed by the language –E.g., first order logic does not express modalities such as possibility Universe Common Logic Z (zed) OWL/ RDF Description Logics Prolog Conceptual Graphs

Intelligent Systems Laboratory Expressivity vs. Computing Complexity Expressivity - set of things that can be represented Computation complexity - amount of computer time needed to process queries, consistency checks, etc. As expressivity goes up, complexity goes up! –Compromises on completeness and soundness reduce complexity –Not as terrible as it sounds!

Intelligent Systems Laboratory ISO WD “the standard” Scope, normative references, terms, symbols Common Logic Core –Abstract syntax Terms, sentences, etc. –Abstract Semantics Interpretation of each CL expression Conformance Three specific surface syntaxes are conformant KIF, CGIF, XCL Provide a mapping from your language to one of those Show that the semantics of your language are preserved for every mapping into CL abstract syntax

Intelligent Systems Laboratory ISO WD Normative Annexes KIF (1st order) –Concrete syntax - KIF – EBNF grammar - –Show KIF to CL abstract semantics CGIF (1st order) –CGIF – EBNF grammar –Show CGIF to CL abstract semantics XCL –XML-based markup – EBNF grammar –Show XCL to CL abstract semantics

Intelligent Systems Laboratory Concrete Syntaxes (  )(Boy(x)  (  )(Girl(y) & Kissed(x,y))) *x] [If: (Boy ?x) [Then: [*y] (Girl ?y) (Kissed ?x ?y) ]] Girl Boy kiss not Boy not Girl

Intelligent Systems Laboratory Common Semantics (forall (?x) ( implies (and (P ?x) (R ?x)) (S ?x)) ) ) ( ∀ x)(P(x)&R(x) → S(x)) CL abstract semantics

Intelligent Systems Laboratory ISO WD Informative Annexes Relationship to other standards and practices –Prolog, Z, OCL (non-ISO-std), OWL (non-ISO-std) –Distinguish CL from Horn clause, description logics, others A benchmark “fact set” of all the kinds of facts that one can have –Especially: meaning of negation, reasoning over sets – see well founded semantics Use Cases

Intelligent Systems Laboratory A Syntactic Form: Conceptual Graphs Introduced by John Sowa in 1984 Focus of seven workshops and twelve international conferences since 1986 Studied by researchers in 11 countries Included in hundreds of research papers published in five languages 13th Intl Conference on Conceptual Structures (ICCS’05) in Kassel, Germany

Intelligent Systems Laboratory ConceptConcept Any distinguishable idea Shown as type-labeled rectangle PERSONPropositionGIVING A person A givingA proposition Some person An instance of giving

Intelligent Systems Laboratory RelationRelation Relation –Relationship between two or more concepts –Shown as oval or circle –The owner of a CAT is a Person. PersonCAT own

Intelligent Systems Laboratory ContextContext A concept that encloses an entire proposition Person: Barb CAT: Albert own Person: Joe Proposition: believe Person “Joe” believes (the proposition) that the owner of the cat “Albert” is Person “Barb”

Intelligent Systems Laboratory Logical Operations AND –Harry is male AND Harry has brown eyes NOT –NOT Harry is a millionaire FORALL –FORALL X in { a, b, c, d }

Intelligent Systems Laboratory Knowledge Interchange Agent AAgent B A’s knowledge base B’s knowledge base A and B, each have a first- order formalization of some knowledge. A and B wish to communicate their knowledge to each other so as to draw some conclusions. Any inferences that B draws from A's input should also be derivable by A, and vice versa Common Logic provides a framework to support this.

Intelligent Systems Laboratory Meaningful Interchange Any meaningful exchange of utterances depends upon the prior existence of an agreed set of semantic and syntactic rules -- ISO TR 9007:1987 (“Helsinki principles”) The recipients of the utterances must use only these rules to interpret the received utterances, if it is to mean the same as that which was meant by the utterer -- ISO TR 9007:1987 (“Helsinki principles”)

Intelligent Systems Laboratory Example: Interchange  CGIF concrete syntax: [Jack: *a] [Jill: *b] (married ?a ?b)  Map  to CL abstract semantics: (married Jack Jill)  Map  to KIF concrete syntax (married (Jack) (Jill) ) Jack Jill married

Intelligent Systems Laboratory Issue - Different Axiomatic Styles A and B may have made divergent assumptions about the logical signatures of their formalizations. –A uses relation name — B uses function –A and B use same relation with different argument orderings or different numbers of arguments. –A particular concept, such as marriage, might be represented by A as an instance of a marriage event, but by B as a relation. Can be solved by mappings between the logical forms of such divergent choices, –CL removes conventional limitations on first-order signatures For example, a name in CL may serve both as an individual name and as a relation name.

Intelligent Systems Laboratory Achieving Semantic Consistency System A: (married Jack Jill) System B: (married (roleset:(husband Jack)(wife Jill))) How does System B “understand” System A? –Provide equivalences to System B ( forall (x y) (implies (married x y) (married (roleset:(husband x) (wife y))) ) )

Intelligent Systems Laboratory Applications of Common Logic Constraints among data elements in a database Semantics of administered items in metadata registries –Bridge the gap between TC 37’s view of a data element and 11179’s view of a data element Ontology definition Automated reasoning and inference

Intelligent Systems Laboratory Database Values As Concepts Single record shows related values only

Intelligent Systems Laboratory Database Values With Semantics